Perceptual Hashing of Video Content Based on Differential Block Similarity

  • Xuebing Zhou
  • Martin Schmucker
  • Christopher L. Brown
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3802)


Each multimedia content can exist in different versions, e.g. different compression rates. Thus, cryptographic hash functions cannot be used for multimedia content identification or verification as they are sensitive to bit flips. In this case, perceptual hash functions that consider perceptual similarity apply. This article describes some of the different existing approaches for video data. One algorithm based on spatio-temporal color difference is investigated. The article shows how this method can be improved by using a simple similarity measure. We analyze the performance of the new method and compare it with the original method. The proposed algorithm shows increased reliability of video identification both in robustness and discriminating capabilities.


Hash Function Video Clip Video Content Multimedia Content Video Annotation 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Serepth, C.J., Uhl, A.: Robust hash function for visual data: an experimental comparison (2003)Google Scholar
  2. 2.
    De Roover, C., De Vleeschouwer, C., Lefebvre, F., Macq, B.: Key-frame radial projection for robust video hashing (2004)Google Scholar
  3. 3.
    Venkatesan, R., Koon, S.-M., Jakubowski, M.H., Moulin, P.: Robust image hashing (2000)Google Scholar
  4. 4.
    Fotopoulos, V., Skodras, A.N.: A new fingerprinting method for digital images (2000)Google Scholar
  5. 5.
    Mucedero, A., Lancini, R., Mapelli, F.: A novel hashing algorithm for video sequences (2004)Google Scholar
  6. 6.
    Caspi, Y., Bargeron, D.: Sharing video annotations (2004)Google Scholar
  7. 7.
    Oostveen, J., Kalker, T., Haitsma, J.: Visual hashing of digital video: applications and techniques (2001)Google Scholar
  8. 8.
    Cheung, S.-c.S., Zakhor, A.: Efficient Video Similarity Measurement with Video Signature (2003)Google Scholar
  9. 9.
    Oostveen, J., Kalker, T., Haitsma, J.: Feature Extraction and a Database Strategy for Video Fingerprint (2002)Google Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2005

Authors and Affiliations

  • Xuebing Zhou
    • 1
  • Martin Schmucker
    • 1
  • Christopher L. Brown
    • 2
  1. 1.Fraunhofer IGDDarmstadtGermany
  2. 2.Institute of TelecommunicationsDarmstadt University of TechnologyDarmstadt

Personalised recommendations